
import matplotlib.pyplot as plt


fig, ax = plt.subplots()


ax.plot()

[]


plt.show()


fig, ax = plt.subplots()
ax.plot([1,2,3], [3,5,0])
plt.show()


fig, ax = plt.subplots()
ax.plot([1,2,3], [3,5,0], 'r--')
plt.show()


fig, ax = plt.subplots()
ax.plot([1,2,3], 'bp') # ΠΡΟΣΟΧΗ! εδώ πλοτάρουμε (1,1), (2,2), (3,3). bp: b=blue, p=pentagon
plt.show()


fig, ax = plt.subplots()
ax.plot([1,2,3], [3,5,0], '--', c="peru")
plt.show()


fig, ax = plt.subplots()
ax.plot([1,2,3], [3,5,0], '--', c="0.8")
plt.show()


fig, ax = plt.subplots()
ax.plot([1,2,3], [3,5,0], '--', c="#876635")
plt.show()


fig, ax = plt.subplots()
ax.plot([1,2,3], [3,5,0], '--', c="#876635")
ax.plot([1, 1.5, 2.5, 4], [3,1, 0, 4], '-', c="magenta", linewidth=5) # Paxos = 5
plt.show()


Μπορούμε επίσης να κάνουμε plot μία συνάρτηση υπολογίζοντας τα Χ και τα Υ της:


def f(x):
    return x**2 # X square

X = [x/10.0 for x in range(1,100)]
Y = [f(x) for x in X]

fig, ax = plt.subplots()
ax.plot(X,Y)
plt.show()


import numpy as np

X = np.linspace(0, 10, 100) # 0 = min X, 10 = max X, 100 = resolution...
Y = [f(x) for x in X]

fig, ax = plt.subplots()
ax.plot(X,Y)

plt.show()


X = np.linspace(0, 10, 5) # 5 = resolution
Y = [f(x) for x in X]

fig, ax = plt.subplots()
ax.plot(X,Y)

plt.show()


fig, ax = plt.subplots()

X = np.linspace(0, 10, 100) # 0 = min X, 10 = max X, 100 = resolution...
Y = [f(x) for x in X]

ax.set_xlim(-5, 15)
ax.set_ylim(-10, 150)
ax.plot(X,Y)

plt.show()


fig, ax = plt.subplots()

X = np.linspace(0, 10, 100) # 0 = min X, 10 = max X, 100 = resolution...
Y = [f(x) for x in X]

ax.set_xlim(-5, 15)
ax.set_ylim(-10, 150)
ax.plot(X,Y)

ax.set_xlabel("Genes")
ax.set_ylabel("Unicorns")
ax.set_title("Number of Unicorns in Genes")


plt.show()


fig, ax = plt.subplots()

X = np.linspace(0, 10, 100) # 0 = min X, 10 = max X, 100 = resolution...
Y = [f(x) for x in X]

ax.set_xlim(-5, 15)
ax.set_ylim(-10, 150)
ax.plot(X,Y)

ax.set_xlabel("Genes")
ax.set_ylabel("Unicorns")
ax.set_title("Number of Unicorns in Genes", fontname="Comic Sans MS", fontsize=20)


plt.show()


fig, ax = plt.subplots()
ax.set_xlim(-5, 15)
ax.set_ylim(-10, 150)
ax.plot(X,Y)
ax.set_xticks([-4,-1,7,8])
plt.show()


fig, ax = plt.subplots()
ax.set_xlim(-5, 15)
ax.set_ylim(-10, 150)
ax.plot(X,Y)
ax.set_xticks([-4,-1,7,8])

ticks = ax.get_xticks().tolist()
ticks[2] = "test"
ax.set_xticklabels(ticks)

plt.show()


fig, ax = plt.subplots()
ax.set_xlim(-5, 15)
ax.set_ylim(-100, 100)
legends = ax.plot(X,Y, 'b', X, [-y for y in Y], 'r')
print (legends)
plt.legend(legends, ["X^2", "-X^2"], loc=2) # loc=2 shmainei panw aristera 
plt.show()

[<matplotlib.lines.Line2D object at 0x10e3c2208>, <matplotlib.lines.Line2D object at 0x10df87d68>]


fig, ax = plt.subplots()
ax.set_xlim(-5, 15)
ax.set_ylim(-100, 100)
legends = ax.plot(X,Y, 'b', X, [-y for y in Y], 'r')

ax.grid(True)

plt.legend(legends, ["X^2", "-X^2"], loc=2) # loc=2 shmainei panw aristera 
plt.show()


fig, ax = plt.subplots()
ax.set_xlim(-5, 15)
ax.set_ylim(-100, 100)
legends = ax.plot(X,Y, 'b', X, [-y for y in Y], 'r')
ax.grid(True)

ax.text(5,0,"Python")

plt.legend(legends, ["X^2", "-X^2"], loc=2) # loc=2 shmainei panw aristera 
plt.show()


fig, ax = plt.subplots()
import random

age = [x for x in range(18,100)]
income = sorted([random.randint(100,1000) for x in age])
percentage_married = sorted([random.randint(0, 80) for x in age])

legends_income = ax.plot(age, income, 'b')

# Dhmiourgoume ena antigrafo tou axona X
ax_new = ax.twinx()

# Plotaroume to deutero plot panw se auto
legends_married = ax_new.plot(age, percentage_married, 'r')

ax.set_xlabel("Age")
ax.set_ylabel("Average income")
ax_new.set_ylabel("Percentage that are married")

plt.show()


fig, ax = plt.subplots()
import random

age = [x for x in range(18,100)]
income = sorted([random.randint(100,1000) for x in age])
percentage_married = sorted([random.randint(0, 80) for x in age])

legends_income = ax.plot(age, income, 'b')

# Dhmiourgoume ena antigrafo tou axona X
ax_new = ax.twinx()

# Plotaroume to deutero plot panw se auto
legends_married = ax_new.plot(age, percentage_married, 'r')

ax.set_xlabel("Age")
ax.set_ylabel("Average income")
ax_new.set_ylabel("Percentage that are married")


# Φτιάχνουμε νέο υπο-πλοτ:
ax_sub = fig.add_axes([0.2, 0.45, 0.18, 0.4])


plt.show()


fig, ax = plt.subplots()
import random

age = [x for x in range(18,100)]
income = sorted([random.randint(100,1000) for x in age])
percentage_married = sorted([random.randint(0, 80) for x in age])

legends_income = ax.plot(age, income, 'b')

# Dhmiourgoume ena antigrafo tou axona X
ax_new = ax.twinx()

# Plotaroume to deutero plot panw se auto
legends_married = ax_new.plot(age, percentage_married, 'r')

ax.set_xlabel("Age")
ax.set_ylabel("Average income")
ax_new.set_ylabel("Percentage that are married")


# Φτιάχνουμε νέο υπο-πλοτ:
ax_sub = fig.add_axes([0.2, 0.45, 0.18, 0.4])
sub_X = np.linspace(1,10,100)
sub_Y = np.sin(sub_X)
ax_sub.plot(sub_X,sub_Y, 'g')

plt.show()


fig, ax = plt.subplots()

X = np.linspace(0,100,1000)
Y = np.exp(X)

ax.plot(X,Y)

ax.set_yscale("log")
plt.show()


fig, ax = plt.subplots()

ax.plot([4,1,10,3])

plt.savefig("figure.png")
plt.savefig("figure.jpg")
plt.savefig("figure.eps")
plt.savefig("figure.tiff")
plt.savefig("figure.pdf")

plt.show()

<matplotlib.figure.Figure at 0x10e54dd68>


def x2(x):
    return x*x

fig, ax = plt.subplots()
leg_orange, = ax.plot([1,2,3], [4,6,5,], '-', color="orange", lw="5", alpha=0.5)
ax.plot([1,2,3], [5,6,1,], '-', color="black", lw="5", alpha=0.5)
ax.plot([1,2,3], [5,6,1,], '*', color="black", lw="5", mew=5)
X = [x/10.0 for x in range(0,100)]
Y = [x2(x) for x in X]
leg_x2, = ax.plot(X, Y)

ticks = ax.get_xticks()
print (ticks)
#ticks = [str(x) + " a " for x in ticks]
# ax.set_xticklabels(ticks)
#ax.set_xticks([x for x in range(-1,5, 2)])
ax.set_xlabel(" GENES ")
ax.set_ylabel(" WHATEVER", fontsize=20)
ax.set_title("The best graph ever")
ax.grid(True)

ax_new = ax.twinx()
ax_new.plot([0,4], [6,6], color="green")
ax_new.set_ylabel("RIGHT LABEL")

ax.set_xlim(-1,4)
ax.set_ylim(0,10)

ax.text(2, 8, "PYTHON!")

plt.legend([leg_x2, leg_orange], ["X^2", "orange"], loc=2)
ax2 = fig.add_axes([0.2, 0.4, 0.2, 0.3])
X2 = [x/10.0 for x in range(0,100)]
Y2 = [3**x for x in X2]
ax2.set_yscale("log")

ax2.plot(X2, Y2)

plt.savefig("figure.png")
plt.savefig("figure.jpg")
plt.savefig("figure.eps")
plt.savefig("figure.tiff")
plt.show()

[  0.   2.   4.   6.   8.  10.]


fig, ax = plt.subplots(3)


fig, ax = plt.subplots(1,3)


fig, ax = plt.subplots(2,3)


ax.shape

(2, 3)


fig, ax = plt.subplots(2,3)
ax[1][2].plot([0,1], [0,1], 'r') # Το κάτω δεξιά
plt.show()


fig, ax = plt.subplots(2,3)
ax[1][2].plot([0,1], [0,1], 'r') # Το κάτω δεξιά
ax[0][1].plot([0,1], [1,0], 'b') # Το πάνω μεσαίο
plt.show()


import random

o = [random.random() for x in range(10)]

def distance(a,b): # Μία απλή συνάρτηση απόστασης: η απόλυτη διαφορά
    return abs(a-b)


# Υπολογίζουμε τις αποστάσεις μεταξύ όλων των ζευγαριών

from itertools import combinations
all_distances = [distance(x,y) for x,y in combinations(o,2)]


# Φτιάχνουμε το δεδρόγραμμα:

from scipy.cluster.hierarchy import dendrogram, linkage

l = linkage(all_distances)
dn = dendrogram(l, orientation='right')


# Μπορούμε να επιβεβαιώσουμε ότι το δενδρόγραμμα είναι σωστό βλέποντας τις τιμές
# Π.χ. οι κόμβοι 7 και 5 είναι το πιο κοντινό ζευγάρι
for i,x in enumerate(o):
    print (i,x)

0 0.535941403079773
1 0.4246640716083616
2 0.6639266980035059
3 0.3177651541741866
4 0.2011939407247051
5 0.7161232515336856
6 0.18269991970324606
7 0.7061565998207685
8 0.022555869599750866
9 0.6362676402564162


import plotly.express as px

fig=px.line(
    x=range(1,11), 
    y=[2,4,6,7,8,9,10,15,18,19], 
    labels={'x':'Day','y':'Number of new cases'})
fig.update_layout({'plot_bgcolor': '#d7dade','paper_bgcolor': '#d7dade'})
fig.show()


fig.write_html("file.html")


import numpy as np

X = np.random.random(100)
Y = np.random.random(100)

fig=px.scatter(
    x=X, 
    y=Y,
    labels={'x':"3'UTR length", 'y':'mean expression level'},
    color=X,
    title='This is a title',
    #log_x=True,
)

fig.update_layout({'plot_bgcolor': '#dedede'})
fig.show()


# Credit: Τζωρτζίνα Νούλη
# Εργασία πάνω στο μάθημα: προγραμματισμός με τη γλώσσα python
# Μεταπτυχιακό πρόγραμμα βιοπληροφορικής, 2020-2021
# Ιατρική σχολή Πανεπιστημίου Κρήτης.

import plotly.graph_objects as go
import numpy as np
from matplotlib import colors as mcolors
import re
from matplotlib import cm

def colors(rgb=False):  
    for mp in ['Accent','tab10_r','Pastel1','Set3',]: #other options 'Pastel1','Dark2','Set1' ,'Set2','Set3','tab10','tab20','Tab20b' etc
        for x in cm.get_cmap(mp).colors:
            yield 'rgb'+str(x) if rgb else x

with open('Arabidopsis_thaliana.TAIR10.49.chromosome.Pt.gff3','r') as f:
    s=f.read().split('\n')  
    s=[i for i in s if i and i[0]!='#']
    l=s[0].split()[4]
    length=int(l)/360
    s=[(int(i.split()[3])/length,int(i.split()[4])/length,i.split()[6],re.findall(r'Name=(.+?);',i.split()[8])[0] if re.findall(r'Name=(.+?);',i.split()[8])!=[] else 'Unnamed'  ,i.split()[2]) for i in s[1:] ]
    groups=set(i[3][:3].lower() for i in s)
    s={k:[v for v in s if v[3][:3].lower()==k] for k in groups }


        

color=colors(True)
fig = go.Figure()

for a,w,h in [(1.06,1,'Strand 2'),(0.94,1,'Strand 1')]:
    fig.add_trace(go.Scatterpolar(r=[a] * 20*len(s),theta=np.linspace(1,360,20*len(s)),mode='lines',
                                  line_color='black', line_width=w,showlegend=False,text=h,hovertemplate=None,hoverinfo='text'))

for a,th,txt in zip([0.8] * (int(l)//1000),np.linspace(0,360,int(l)//1000),[int(x) for x in range(0,int(l)//1000 )]):
    if not txt%5:
        fig.add_trace(go.Scatterpolar(r=[a],theta=[th],textfont={'family':"PT Sans Narrow"},mode='markers',line_color='#383838', line_width=0.1,showlegend=False,text=str(txt)+' kb',hovertemplate=None,hoverinfo='text'))

for group in s:
    c=next(color)
    for i in s[group]:
        if i[3]!='Unnamed' and i[4]!='exon':
            fig.add_trace(go.Scatterpolar(r=[1.06*(i[2]=='-')+0.94*(i[2]=='+')] * 20,theta = np.linspace(i[0], i[1],20),mode='lines',line_width=15,name= i[3],line_color=c,hoverinfo='text+name',hovertemplate=None,text=i[3]))
        
        ### Alternative version of plot with coding regions for mRNA,tRNA,rRNA  (only one version should be active or they'll overlap)
        
        #if i[3]=='Unnamed' and i[4]!='CDS':
        #    col={'mRNA':'Burlywood','tRNA':'olive','rRNA':'tomato'}
        #    fig.add_trace(go.Scatterpolar(r=[1.06*(i[2]=='-')+0.94*(i[2]=='+')] * 20,theta = np.linspace(i[0], i[1],20),mode='lines',line_width=15,name= i[4],line_color=col[i[4]] ,hoverinfo='text+name',hovertemplate=None,text=i[4]))
        

fig.update_layout(
  polar=dict(angularaxis=dict(rotation=90,direction="counterclockwise",showticklabels=False,showgrid=False),radialaxis=dict(range=[0, 1.16],showticklabels=False,visible=False)))   

fig.show()


import seaborn as sns


penguins = sns.load_dataset("penguins")
sns.displot(data=penguins, x="flipper_length_mm")

<seaborn.axisgrid.FacetGrid at 0x7ffb595c7f40>


import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([0,1,2,3], [1,3,5,4], '-')
plt.show()


import seaborn as sns
sns.set_theme()

fig, ax = plt.subplots()
ax.plot([0,1,2,3], [1,3,5,4], '-')
plt.show()


import random

import networkx as nx

G = nx.Graph()

labels = []

# 10 τυχαίες ακμές
for x in range(10):
    n1 = random.randint(1,10) # ξεκινάμε από έναν τυχαίο κόμβο
    n2 = random.randint(1,10) # Καταλίγουμε σε έναν τυχαίο κόμβο
    c = random.random() # Τυχαίο χρώμα
    G.add_edge(n1, n2, 
               color=c, 
               weight= '{0:.2f}'.format(random.random()), # Τυχαίο βάρος στην ακμή
              )
    labels.append('d')
    
pos = nx.spring_layout(G) # Υπολογίζουμε τις θέσεις στον 2διάστατο χόρο των κόμβων

node_size = {x:x/2 for x in range(1,11)} # Υπολογίζουμε (αυθαίρετα) το μέγεθος των κόμβν

# Τα κάνουμε plot
nx.draw(
    G,
    pos,
    with_labels=False,
    node_size=[50 if i%2==0 else 40 for i in G.nodes()],
    edge_color=nx.get_edge_attributes(G,'color').values(),
    node_color=['#525050' if i%2==0 else '#FF0000' for i in G.nodes()],
)    
labels = nx.get_edge_attributes(G,'weight')  # Βάζουμε τα βάρη πάνω στις ακμές
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels) # πλοτάρουμε τα βάρη στις ακμές:

#plt.figure(3,figsize=(15,10))

{(9, 6): Text(-0.3246399734880249, 0.8393607649129553, '0.72'),
 (9, 1): Text(-0.13822768754437625, 0.6171603138000668, '0.89'),
 (6, 6): Text(-0.41265536497435334, 0.9446971915222568, '0.39'),
 (1, 3): Text(-0.00752602109411178, 0.2757609811871292, '0.46'),
 (1, 8): Text(0.06323129245690765, 0.5789424219216375, '0.79'),
 (3, 7): Text(0.04699044479343656, -0.12170415825792096, '0.78'),
 (4, 2): Text(0.018412541670912178, -0.796599028576672, '0.36'),
 (4, 5): Text(0.2498097374056697, -0.8219724442684326, '0.21'),
 (4, 7): Text(0.08840511218292141, -0.469289439065243, '0.73'),
 (2, 2): Text(-0.0707830023359778, -0.9492531686164785, '0.99')}


import geopandas
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))


def new_function(row):
    name = row['name']
    if name == 'Greece':
        return 100
    if name == 'Italy':
        return 200
    if name == 'Germany':
        return 300
    
    return 0

world['new_column'] = world.apply(new_function, axis=1)


import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable

fig, ax = plt.subplots(1, 1, figsize=(10, 8))
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)
world.plot(column='new_column', ax=ax, legend=True, cax=cax)

<AxesSubplot:>


# Credit: Τζωρτζίνα Νούλη
# Εργασία πάνω στο μάθημα: προγραμματισμός με τη γλώσσα python
# Μεταπτυχιακό πρόγραμμα βιοπληροφορικής, 2020-2021
# Ιατρική σχολή Πανεπιστημίου Κρήτης.

import re
import gzip
import numpy as np
from collections import defaultdict
import geopandas
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib import cm
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import plotly.express as px

def plot_geo(file,interactive=False):
    with gzip.open(file, 'rt') as f:

        d=defaultdict(int)
        for i in f:
            s=re.findall('>hCoV-19/(.+?)/.+',i)
            if s:
                d[s[0]]+=1
    world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
    world['Covid_Count'] = world.apply(lambda row:d[row['name']], axis=1)
    if not interactive:
        fig, ax = plt.subplots(1, 1, figsize=(14, 12))
        divider = make_axes_locatable(ax)
        cax = divider.append_axes("right", size="4%", pad=0.2)
        ax=world.plot(column='Covid_Count',ax=ax,legend_kwds={'label': 'Number of Covid sequences in data','orientation':"vertical"},vmin=1, legend=True, cax=cax,cmap='nipy_spectral_r')
        world.boundary.plot(figsize=(14,12),edgecolor='darkgrey',ax=ax)
    else:
        fig = px.choropleth_mapbox(world, geojson=world.geometry, locations=world.index, color='Covid_Count',
                           color_continuous_scale="inferno_r",hover_name='name',
                           mapbox_style="carto-positron",
                           zoom=3, center = {"lat": 37.0902, "lon": -95.7129},
                           opacity=0.5)

        fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
        fig.show()


file = 'covid_fasta.gz'
#plot_geo(file)

plot_geo(file,interactive=True)

Ploting με τη βιβλιοθήκη matplotlib¶

Το πλοτάρισμα είναι τέχνη!¶

subplots¶

Κάποιες σημειώσεις:¶

Dendrogram¶

plotly¶

Seaborn¶

Networkx¶

GeoPandas¶

plotly GEO interactive¶